OpenRaiser/Intern-Atlas huggingface dataset:
Towards an Automated Multimodal Approach for Video Summarization: Building a Bridge Between Text, Audio and Facial Cue-Based Summarization
Venue: arXiv cs.CV 2025
Year: 2025

The increasing volume of video content in educational, professional, and social domains necessitates effective summarization techniques that go beyond traditional unimodal approaches. This paper proposes a behaviour-aware multimodal video summarization framework that integrates textual, audio, and visual cues to generate timestamp-aligned summaries. By extracting prosodic features, textual cues and visual indicators, the framework identifies semantically and emotionally important moments. A key contribution is the identification of bonus words, which are terms emphasized across multiple modalities and used to improve the semantic relevance and expressive clarity of the summaries. The approach is evaluated against pseudo-ground truth (pGT) summaries generated using LLM-based extractive method. Experimental results demonstrate significant improvements over traditional extractive method, such as the Edmundson method, in both text and video-based evaluation metrics. Text-based metrics show ROUGE-1 increasing from 0.4769 to 0.7929 and BERTScore from 0.9152 to 0.9536, while in video-based evaluation, our proposed framework improves F1-Score by almost 23%. The findings underscore the potential of multimodal integration in producing comprehensive and behaviourally informed video summaries.
Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation
Venue: arXiv cs.NE 2025
Year: 2025

Multi-bit spiking neural networks (SNNs) have recently become a heated research spot, pursuing energy-efficient and high-accurate AI. However, with more bits involved, the associated memory and computation demands escalate to the point where the performance improvements become disproportionate. Based on the insight that different layers demonstrate different importance and extra bits could be wasted and interfering, this paper presents an adaptive bit allocation strategy for direct-trained SNNs, achieving fine-grained layer-wise allocation of memory and computation resources. Thus, SNN's efficiency and accuracy can be improved. Specifically, we parametrize the temporal lengths and the bit widths of weights and spikes, and make them learnable and controllable through gradients. To address the challenges caused by changeable bit widths and temporal lengths, we propose the refined spiking neuron, which can handle different temporal lengths, enable the derivation of gradients for temporal lengths, and suit spike quantization better. In addition, we theoretically formulate the step-size mismatch problem of learnable bit widths, which may incur severe quantization errors to SNN, and accordingly propose the step-size renewal mechanism to alleviate this issue. Experiments on various datasets, including the static CIFAR and ImageNet datasets and the dynamic CIFAR-DVS and DVS-GESTURE datasets, demonstrate that our methods can reduce the overall memory and computation cost while achieving higher accuracy. Particularly, our SEWResNet-34 can achieve a 2.69\% accuracy gain and 4.16$\times$ lower bit budgets over the advanced baseline work on ImageNet. This work is open-sourced at \href{https://github.com/Ikarosy/Towards-Efficient-and-Accurate-Spiking-Neural-Networks-via-Adaptive-Bit-Allocation}{this link}.
A comprehensive control architecture for semi-autonomous dual-arm robots in agriculture settings
Venue: arXiv cs.RO 2025
Year: 2025

The adoption of mobile robotic platforms in complex environments, such as agricultural settings, requires these systems to exhibit a flexible yet effective architecture that integrates perception and control. In such scenarios, several tasks need to be accomplished simultaneously, ranging from managing robot limits to performing operational tasks and handling human inputs. The purpose of this paper is to present a comprehensive control architecture for achieving complex tasks such as robotized harvesting in vineyards within the framework of the European project CANOPIES. In detail, a 16-DOF dual-arm mobile robot is employed, controlled via a Hierarchical Quadratic Programming (HQP) approach capable of handling both equality and inequality constraints at various priorities to harvest grape bunches selected by the perception system developed within the project. Furthermore, given the complexity of the scenario and the uncertainty in the perception system, which could potentially lead to collisions with the environment, the handling of interaction forces is necessary. Remarkably, this was achieved using the same HQP framework. This feature is further leveraged to enable semi-autonomous operations, allowing a human operator to assist the robotic counterpart in completing harvesting tasks. Finally, the obtained results are validated through extensive testing conducted first in a laboratory environment to prove individual functionalities, then in a real vineyard, encompassing both autonomous and semi-autonomous grape harvesting operations.
AutoEvoEval: An Automated Framework for Evolving Close-Ended LLM Evaluation Data
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have shown remarkable performance on various tasks, but existing evaluation benchmarks are often static and insufficient to fully assess their robustness and generalization in realistic scenarios. Prior work using evolutionary or adversarial data augmentation has improved evaluation diversity but lacks systematic control over perturbation types and multi-step complexity, limiting comprehensive robustness analysis. To address these gaps, we propose AutoEvoEval, an evolution-based evaluation framework for close-ended tasks such as multi-choice question answering. AutoEvoEval introduces 22 interpretable atomic evolution operations and supports multi-round compositions, enabling controlled generation of diverse, challenging, and realistic test samples. We conduct extensive experiments addressing four research questions on a broad set of open- and closed-source LLMs. Our results show that atomic operations cause an average accuracy drop of 7.283\%, with structure-disrupting or misleading semantic edits causing the largest declines. Model sensitivities vary significantly for the same perturbation, and combining multiple evolution steps amplifies adversarial effects by up to 52.932\%. These findings suggest current benchmarks may overestimate true model generalization and emphasize the need for evolution-aware robustness evaluation. Code and resources are available at: https://github.com/SYSUSELab/AutoEvoEval.
Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model
Venue: arXiv cs.RO 2025
Year: 2025

We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development.
BayesL: Towards a Logical Framework for Bayesian Networks
Venue: arXiv cs.AI 2025
Year: 2025

We introduce BayesL, a novel logical framework for specifying, querying, and verifying the behaviour of Bayesian networks (BNs). BayesL (pronounced "Basil") is a structured language that allows for the creation of queries over BNs. It facilitates versatile reasoning concerning causal and evidence-based relationships, and permits comprehensive what-if scenario evaluations without the need for manual modifications to the model.
Leveraging a Multi-Agent LLM-Based System to Educate Teachers in Hate Incidents Management
Venue: arXiv cs.CY 2025
Year: 2025

Computer-aided teacher training is a state-of-the-art method designed to enhance teachers' professional skills effectively while minimising concerns related to costs, time constraints, and geographical limitations. We investigate the potential of large language models (LLMs) in teacher education, using a case of teaching hate incidents management in schools. To this end, we create a multi-agent LLM-based system that mimics realistic situations of hate, using a combination of retrieval-augmented prompting and persona modelling. It is designed to identify and analyse hate speech patterns, predict potential escalation, and propose effective intervention strategies. By integrating persona modelling with agentic LLMs, we create contextually diverse simulations of hate incidents, mimicking real-life situations. The system allows teachers to analyse and understand the dynamics of hate incidents in a safe and controlled environment, providing valuable insights and practical knowledge to manage such situations confidently in real life. Our pilot evaluation demonstrates teachers' enhanced understanding of the nature of annotator disagreements and the role of context in hate speech interpretation, leading to the development of more informed and effective strategies for addressing hate in classroom settings.
Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
Venue: arXiv cs.CL 2025
Year: 2025

Large Reasoning Models (LRMs) excel at solving complex problems but face an overthinking dilemma. When handling simple tasks, they often produce verbose responses overloaded with thinking tokens (e.g., wait, however). These tokens trigger unnecessary high-level reasoning behaviors like reflection and backtracking, reducing efficiency. In this work, our pilot study reveals that these thinking-token-induced behaviors are not essential for effective problem-solving and may even hinder correct reasoning within constrained token budgets. We identify this phenomenon as the thinking trap. To mitigate this issue, we propose Dual Policy Preference Optimization (DuP-PO), a novel algorithm featuring: (1) A rollout sampling strategy that guarantees balanced exposure to responses with and without thinking tokens; (2) A fine-grained advantage control technique to dynamically regulate the prediction of target tokens; (3) A policy shaping method ensuring stable gradient contributions from thinking tokens. Experimental results on five popular math reasoning benchmarks show that DuP-PO performs well on the popular LRM, which significantly improves their token efficiency during reasoning, while achieving superior performance of the base model.
Use Sparse Autoencoders to Discover Unknown Concepts, Not to Act on Known Concepts
Venue: arXiv cs.LG 2025
Year: 2025

While sparse autoencoders (SAEs) have generated significant excitement, a series of negative results have added to skepticism about their usefulness. Here, we establish a conceptual distinction that reconciles competing narratives surrounding SAEs. We argue that while SAEs may be less effective for acting on known concepts, SAEs are powerful tools for discovering unknown concepts. This distinction cleanly separates existing negative and positive results, and suggests several classes of SAE applications. Specifically, we outline use cases for SAEs in (i) ML interpretability, explainability, fairness, auditing, and safety, and (ii) social and health sciences.
Email as the Interface to Generative AI Models: Seamless Administrative Automation
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces a novel architectural framework that integrates Large Language Models (LLMs) with email interfaces to automate administrative tasks, specifically targeting accessibility barriers in enterprise environments. The system connects email communication channels with Optical Character Recognition (OCR) and intelligent automation, enabling non-technical administrative staff to delegate complex form-filling and document processing tasks using familiar email interfaces. By treating the email body as a natural language prompt and attachments as contextual information, the workflow bridges the gap between advanced AI capabilities and practical usability. Empirical evaluation shows that the system can complete complex administrative forms in under 8 seconds of automated processing, with human supervision reducing total staff time by a factor of three to four compared to manual workflows. The top-performing LLM accurately filled 16 out of 29 form fields and reduced the total cost per processed form by 64% relative to manual completion. These findings demonstrate that email-based LLM integration is a viable and cost-effective approach for democratizing advanced automation in organizational settings, supporting widespread adoption without requiring specialized technical knowledge or major workflow changes. This aligns with broader trends in leveraging LLMs to enhance accessibility and automate complex tasks for non-technical users, making technology more inclusive and efficient.
Comparative Studies: Cloud-Enabled Adaptive Learning System for Scalable Education in Sub-Saharan
Venue: arXiv cs.CY 2025
Year: 2025

The integration of cloud computing in education can revolutionise learning in advanced (Australia & South Korea) and middle-income (Ghana & Nigeria) countries, while offering scalable, cost-effective and equitable access to adaptive learning systems. This paper explores how cloud computing and adaptive learning technologies are deployed across different socio-economic and infrastructure contexts. The study identifies enabling factors and systematic challenges, providing insights into how cloud-based education can be tailored to bridge the digital and educational divide globally.
VMoBA: Mixture-of-Block Attention for Video Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

The quadratic complexity of full attention mechanisms poses a significant bottleneck for Video Diffusion Models (VDMs) aiming to generate long-duration, high-resolution videos. While various sparse attention methods have been proposed, many are designed as training-free inference accelerators or do not optimally capture the unique spatio-temporal characteristics inherent in video data when trained natively. This paper introduces Video Mixture of Block Attention (VMoBA), a novel sparse attention mechanism specifically adapted for VDMs. Motivated by an in-depth analysis of attention patterns within pre-trained video transformers, which revealed strong spatio-temporal locality, varying query importance, and head-specific concentration levels, VMoBA enhances the original MoBA framework with three key modifications: (1) a layer-wise recurrent block partition scheme (1D-2D-3D) to dynamically adapt to diverse spatio-temporal attention patterns and improve efficiency; (2) global block selection to prioritize the most salient query-key block interactions across an entire attention head; and (3) threshold-based block selection to dynamically determine the number of attended blocks based on their cumulative similarity. Extensive experiments demonstrate that VMoBA significantly accelerates the training of VDMs on longer sequences, achieving 2.92x FLOPs and 1.48x latency speedup, while attaining comparable or even superior generation quality to full attention. Furthermore, VMoBA exhibits competitive performance in training-free inference, offering 2.40x FLOPs and 1.35x latency speedup for high-res video generation.
IMPACT: Inflectional Morphology Probes Across Complex Typologies
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have shown significant progress on various multilingual benchmarks and are increasingly used to generate and evaluate text in non-English languages. However, while they may produce fluent outputs, it remains unclear to what extent these models truly grasp the underlying linguistic complexity of those languages, particularly in morphology. To investigate this, we introduce IMPACT, a synthetically generated evaluation framework focused on inflectional morphology, which we publicly release, designed to evaluate LLM performance across five morphologically rich languages: Arabic, Russian, Finnish, Turkish, and Hebrew. IMPACT includes unit-test-style cases covering both shared and language-specific phenomena, from basic verb inflections (e.g., tense, number, gender) to unique features like Arabic's reverse gender agreement and vowel harmony in Finnish and Turkish. We assess eight multilingual LLMs that, despite strong English performance, struggle with other languages and uncommon morphological patterns, especially when judging ungrammatical examples. We also show that Chain of Thought and Thinking Models can degrade performance. Our work exposes gaps in LLMs' handling of linguistic complexity, pointing to clear room for improvement. To support further research, we publicly release the IMPACT framework.
AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models
Venue: arXiv cs.AI 2025
Year: 2025

Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'frontier models' (typically all referred to hereafter with the umbrella term 'GPAI/foundation models' except where greater specificity is needed), can provide many beneficial capabilities but also risks of adverse events with profound consequences. This document provides risk-management practices or controls for identifying, analyzing, and mitigating risks of GPAI/foundation models. We intend this document primarily for developers of large-scale, state-of-the-art GPAI/foundation models; others that can benefit from this guidance include downstream developers of end-use applications that build on a GPAI/foundation model. This document facilitates conformity with or use of leading AI risk management-related standards, adapting and building on the generic voluntary guidance in the NIST AI Risk Management Framework and ISO/IEC 23894, with a focus on the unique issues faced by developers of GPAI/foundation models.
Green Metrics Tool: Measuring for fun and profit
Venue: arXiv cs.SE 2025
Year: 2025

The environmental impact of software is gaining increasing attention as the demand for computational resources continues to rise. In order to optimize software resource consumption and reduce carbon emissions, measuring and evaluating software is a first essential step. In this paper we discuss what metrics are important for fact base decision making. We introduce the Green Metrics Tool (GMT), a novel framework for accurately measuring the resource consumption of software. The tool provides a containerized, controlled, and reproducible life cycle-based approach, assessing the resource use of software during key phases. Finally, we discuss GMT features like visualization, comparability and rule- and LLM-based optimisations highlighting its potential to guide developers and researchers in reducing the environmental impact of their software.
Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance
Venue: arXiv cs.CV 2025
Year: 2025

Understanding why a classification model prefers one class over another for an input instance is the challenge of contrastive explanation. This work implements concept-based contrastive explanations for image classification by leveraging the similarity of instance embeddings and relevance of human-understandable concepts used by a fine-tuned deep learning model. Our approach extracts concepts with their relevance score, computes contrasts for similar instances, and evaluates the resulting contrastive explanations based on explanation complexity. Robustness is tested for different image augmentations. Two research questions are addressed: (1) whether explanation complexity varies across different relevance ranges, and (2) whether explanation complexity remains consistent under image augmentations such as rotation and noise. The results confirm that for our experiments higher concept relevance leads to shorter, less complex explanations, while lower relevance results in longer, more diffuse explanations. Additionally, explanations show varying degrees of robustness. The discussion of these findings offers insights into the potential of building more interpretable and robust AI systems.
Fantastic Flips and Where to Find Them: A General Framework for Parameterized Local Search on Partitioning Problem
Venue: arXiv cs.DS 2025
Year: 2025

Parameterized local search combines classic local search heuristics with the paradigm of parameterized algorithmics. While most local search algorithms aim to improve given solutions by performing one single operation on a given solution, the parameterized approach aims to improve a solution by performing $k$ simultaneous operations. Herein, $k$ is a parameter called search radius for which the value can be chosen by a user. One major goal in the field of parameterized local search is to outline the trade-off between the size of $k$ and the running time of the local search step. In this work, we introduce an abstract framework that generalizes natural parameterized local search approaches for a large class of partitioning problems: Given $n$ items that are partitioned into $b$ bins and a target function that evaluates the quality of the current partition, one asks whether it is possible to improve the solution by removing up to $k$ items from their current bins and reassigning them to other bins. Among others, our framework applies for the local search versions of problems like Cluster Editing, Vector Bin Packing, and Nash Social Welfare. Motivated by a real-world application of the problem Vector Bin Packing, we introduce a parameter called number of types $τ\le n$ and show that all problems fitting in our framework can be solved in $τ^k 2^{O(k)} |I|^{O(1)}$ time, where $|I|$ denotes the total input size. In case of Cluster Editing, the parameter $τ$ generalizes the well-known parameter neighborhood diversity of the input graph. We complement this by showing that for all considered problems, an algorithm significantly improving over our algorithm with running time $τ^k 2^{O(k)} |I|^{O(1)}$ would contradict the ETH. Additionally, we show that even on very restricted instances, all considered problems are W[1]-hard when parameterized by the search radius $k$ alone.
Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems
Venue: arXiv cs.IT 2025
Year: 2025

Large artificial intelligence (AI) models offer revolutionary potential for future wireless systems, promising unprecedented capabilities in network optimization and performance. However, current paradigms largely overlook crucial physical interactions. This oversight means they primarily rely on offline datasets, leading to difficulties in handling real-time wireless dynamics and non-stationary environments. Furthermore, these models often lack the capability for active environmental probing. This paper proposes a fundamental paradigm shift towards wireless embodied large AI (WELAI), moving from passive observation to active embodiment. We first identify key challenges faced by existing models, then we explore the design principles and system structure of WELAI. Besides, we outline prospective applications in next-generation wireless. Finally, through an illustrative case study, we demonstrate the effectiveness of WELAI and point out promising research directions for realizing adaptive, robust, and autonomous wireless systems.